Abstract

Gene expression has a strong circadian component, with oscillations in many genes exceeding the variation between individuals. If circadian gene expression patterns were as well described for humans as they are for animals used as model organisms, it might be possible to design more accurate, “time-adjusted” biomarkers of disease and to determine optimal times for administering drugs. However, the usual way of detecting circadian gene expression is through serial tissue biopsies, which is impractical and risky in patients. An alternate approach is to reconstruct circadian rhythms in gene expression from a large group of individuals by analyzing biopsy specimens that were obtained throughout the day and night. While there are plenty of clinical specimen repositories that are large enough to do the job, sample collection times were almost never recorded, and without this information the circadian component of gene expression cannot be accessed. To address this problem, Anafi et al. developed an algorithm called CYCLOPS (Cyclic Ordering by Periodic Structure), which can infer the collection time of biopsy specimens relative to an idealized 24-hour day. Their informatics approach utilizes data from large gene expression profiling projects (>250 samples) and appears most useful in specimens from highly rhythmic tissues, such as liver, kidney, and lung. To validate CYCLOPS, the authors showed that it could accurately determine time of death from prefrontal cortex biopsies obtained at autopsy. They went on to generate human circadian transcriptomes for a variety of organs and compared rhythmic gene expression in liver cancer biopsies with that of the healthy liver tissue at the tumor margin. Like all new informatics tools, more work will be needed to validate CYCLOPS and to better understand its strengths and limitations. However, the ability to unmask temporal information from a single biopsy specimen would be a major advance and promises to accelerate the application of circadian biology to human disease.